Name: Java Deep Learning Projects
Author: Md. Rezaul Karim
ISBN: 9781788996525

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Java Deep Learning Projects

Md. Rezaul Karim

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Contributors
About the author
About the reviewer
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
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Conventions used
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Getting Started with Deep Learning
A soft introduction to ML
Working principles of ML algorithms
Supervised learning
Unsupervised learning
Reinforcement learning
Putting ML tasks altogether
Delving into deep learning
How did DL take ML into next level?
Artificial Neural Networks
Biological neurons
A brief history of ANNs
How does an ANN learn?
ANNs and the backpropagation algorithm
Forward and backward passes
Weights and biases
Weight optimization
Activation functions
Neural network architectures
Deep neural networks
Multilayer Perceptron
Deep belief networks
Autoencoders
Convolutional neural networks
Recurrent neural networks
Emergent architectures
Residual neural networks
Generative adversarial networks
Capsule networks
DL frameworks and cloud platforms
Deep learning frameworks
Cloud-based platforms for DL
Deep learning from a disaster – Titanic survival prediction
Problem description
Configuring the programming environment
Feature engineering and input dataset preparation
Training MLP classifier
Evaluating the MLP classifier
Frequently asked questions (FAQs)
Summary
Answers to FAQs
Cancer Types Prediction Using Recurrent Type Networks
Deep learning in cancer genomics
Cancer genomics dataset description
Preparing programming environment
Titanic survival revisited with DL4J
Multilayer perceptron network construction
Hidden layer 1
Hidden layer 2
Output layer
Network training
Evaluating the model
Cancer type prediction using an LSTM network
Dataset preparation for training
Recurrent and LSTM networks
Dataset preparation
LSTM network construction
Network training
Evaluating the model
Frequently asked questions (FAQs)
Summary
Answers to questions
Multi-Label Image Classification Using Convolutional Neural Networks
Image classification and drawbacks of DNNs
CNN architecture
Convolutional operations
Pooling and padding operations
Fully connected layer (dense layer)
Multi-label image classification using CNNs
Problem description
Description of the dataset
Removing invalid images
Workflow of the overall project
Image preprocessing
Extracting image metadata
Image feature extraction
Preparing the ND4J dataset
Training, evaluating, and saving the trained CNN models
Network construction
Scoring the model
Submission file generation
Wrapping everything up by executing the main() method
Frequently asked questions (FAQs)
Summary
Answers to questions
Sentiment Analysis Using Word2Vec and LSTM Network
Sentiment analysis is a challenging task
Using Word2Vec for neural word embeddings
Datasets and pre-trained model description
Large Movie Review dataset for training and testing
Folder structure of the dataset
Description of the sentiment labeled dataset
Word2Vec pre-trained model
Sentiment analysis using Word2Vec and LSTM
Preparing the train and test set using the Word2Vec model
Network construction, training, and saving the model
Restoring the trained model and evaluating it on the test set
Making predictions on sample review texts
Frequently asked questions (FAQs)
Summary
Answers to questions
Transfer Learning for Image Classification
Image classification with pretrained VGG16
DL4J and transfer learning
Developing an image classifier using transfer learning
Dataset collection and description
Architecture choice and adoption
Train and test set preparation
Network training and evaluation
Restoring the trained model and inferencing
Making simple inferencing
Frequently asked questions (FAQs)
Summary
Answers to questions
Real-Time Object Detection using YOLO, JavaCV, and DL4J
Object detection from images and videos
Object classification, localization, and detection
Convolutional Sliding Window (CSW)
Object detection from videos
You Only Look Once (YOLO)
Developing a real-time object detection project
Step 1 – Loading a pre-trained YOLO model
Step 2 – Generating frames from video clips
Step 3 – Feeding generated frames into Tiny YOLO model
Step 4 – Object detection from image frames
Step 5 – Non-max suppression in case of more than one bounding box
Step 6 – wrapping up everything and running the application
Frequently asked questions (FAQs)
Summary
Answers to questions
Stock Price Prediction Using LSTM Network
State-of-the-art automated stock trading
Developing a stock price predictive model
Data collection and exploratory analysis
Preparing the training and test sets
LSTM network construction
Network training, and saving the trained model
Restoring the saved model for inferencing
Evaluating the model
Frequently asked questions (FAQs)
Summary
Answers to questions
Distributed Deep Learning – Video Classification Using Convolutional LSTM Networks
Distributed deep learning across multiple GPUs
Distributed training on GPUs with DL4J
Video classification using convolutional – LSTM
UCF101 – action recognition dataset
Preprocessing and feature engineering
Solving the encoding problem
Data processing workflow
Simple UI for checking video frames
Preparing training and test sets
Network creation and training
Performance evaluation
Distributed training on AWS deep learning AMI 9.0
Frequently asked questions (FAQs)
Summary
Answers to questions
Playing GridWorld Game Using Deep Reinforcement Learning
Notation, policy, and utility for RL
Notations in reinforcement learning
Policy
Utility
Neural Q-learning
Introduction to QLearning
Neural networks as a Q-function
Developing a GridWorld game using a deep Q-network
Generating the grid
Calculating agent and goal positions
Calculating the action mask
Providing guidance action
Calculating the reward
Flattening input for the input layer
Network construction and training
Playing the GridWorld game
Frequently asked questions (FAQs)
Summary
Answers to questions
Developing Movie Recommendation Systems Using Factorization Machines
Recommendation systems
Recommendation approaches
Collaborative filtering approaches
Content-based filtering approaches
Hybrid recommender systems
Model-based collaborative filtering
The utility matrix
The cold-start problem in collaborative-filtering approaches
Factorization machines in recommender systems
Developing a movie recommender system using FMs
Dataset description and exploratory analysis
Movie rating prediction
Converting the dataset into LibFM format
Training and test set preparation
Movie rating prediction
Which one makes more sense ;– ranking or rating?
Frequently asked questions (FAQs)
Summary
Answers to questions
Discussion, Current Trends, and Outlook
Discussion and outlook
Discussion on the completed projects
Titanic survival prediction using MLP and LSTM networks
Cancer type prediction using recurrent type networks
Image classification using convolutional neural networks
Sentiment analysis using Word2Vec and the LSTM network
Image classification using transfer learning
Real-time object detection using YOLO, JavaCV, and DL4J
Stock price prediction using LSTM network
Distributed deep learning – video classification using a convolutional-LSTM network
Using deep reinforcement learning for GridWorld
Movie recommender system using factorization machines
Current trends and outlook
Current trends
Outlook on emergent DL architectures
Residual neural networks
GANs
Capsule networks (CapsNet)
Semantic image segmentation
Deep learning for clustering analysis
Frequently asked questions (FAQs)
Answers to questions
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Java Deep Learning Projects